Gene expression-based drug repurposing to target aging

Handan Melike Dönertaş, Matías Fuentealba Valenzuela, Linda Partridge, Janet M Thornton, Handan Melike Dönertaş, Matías Fuentealba Valenzuela, Linda Partridge, Janet M Thornton

Abstract

Aging is the largest risk factor for a variety of noncommunicable diseases. Model organism studies have shown that genetic and chemical perturbations can extend both lifespan and healthspan. Aging is a complex process, with parallel and interacting mechanisms contributing to its aetiology, posing a challenge for the discovery of new pharmacological candidates to ameliorate its effects. In this study, instead of a target-centric approach, we adopt a systems level drug repurposing methodology to discover drugs that could combat aging in human brain. Using multiple gene expression data sets from brain tissue, taken from patients of different ages, we first identified the expression changes that characterize aging. Then, we compared these changes in gene expression with drug-perturbed expression profiles in the Connectivity Map. We thus identified 24 drugs with significantly associated changes. Some of these drugs may function as antiaging drugs by reversing the detrimental changes that occur during aging, others by mimicking the cellular defence mechanisms. The drugs that we identified included significant number of already identified prolongevity drugs, indicating that the method can discover de novo drugs that meliorate aging. The approach has the advantages that using data from human brain aging data, it focuses on processes relevant in human aging and that it is unbiased, making it possible to discover new targets for aging studies.

Keywords: aging; drug repurposing; gene expression; the Connectivity Map.

© 2018 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.

Figures

Figure 1
Figure 1
(a) Age distribution of the brains from which the data sets used in the study were derived. The error bars show the standard deviation of the sample frequency for different brain regions in data sources with multiple brain regions. (b) Hypothetical gene expression plots, demonstrating how Spearman's correlation coefficient and p‐value behave when the association is weak or nonmonotonic. (c) Pairwise Spearman's rank correlation coefficients across data sets. The intensity of the colours on the heatmap shows the magnitude of the correlation coefficient
Figure 2
Figure 2
Method summary for (a) compiling the aging signature and (b) the CMap algorithm
Figure 3
Figure 3
Gene Ontology Biological Process Categories significantly enriched in (a) down‐ and (b) upregulated genes in the microarray aging signature. Red circles represent the genes, and diamonds show the significantly associated GO categories, where FDR adjusted p < 0.05. The size of the diamonds represents the effect size (odds ratio)
Figure 4
Figure 4
Similarity score table for the drugs having at least one significant association with the aging signatures. Each row corresponds to a drug and columns correspond to two independent aging signatures—using the microarray and the GTEx data sets. The size of score labels indicates the significance of the results (FDR‐corrected p < 0.05). The row labels written in bold indicates the drugs in the DrugAge database
Figure 5
Figure 5
Schematic representation of the drug–target associations as a network. Blue and red nodes show drugs and targets, respectively. The drugs with a light blue background are present in DrugAge database and the targets with a pink background are in either GenAge model organism or GenAge human databases

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